BDAS 2015, BDAS 2016: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery pp 659-667 | Cite as
AI Implementation in Military Combat Identification – A Practical Solution
Conference paper
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Abstract
This paper presents the architecture of a communication system which was implemented in MiG-29 airplanes. This system provides a continuous on-line access to the situational awareness information which is necessary for the pilot. The interoperability of this system with other NATO systems allows to collect and transfer data between them. Artificial Intelligence methods are used to implement and improve this system. This modification enables the system to work faster and increases the situational awareness of the pilot on the battlefield.
Keywords
CID Security Network Artificial intelligenceReferences
- 1.Biuletyn konstrukcyjny p/o/r/u/5034/k/08Google Scholar
- 2.Angryk, R.A., Czerniak, J.: Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases. Int. J. Approximate Reasoning 51(8), 895–911 (2010)CrossRefGoogle Scholar
- 3.Apiecionek, Ł., Romantowski, M.: Secure IP network model. Comput. Method Sci. Technol. 4, 209–213 (2013)CrossRefGoogle Scholar
- 4.Apiecionek, Ł., Romantowski, M., Śliwa, J., Jasiul, B., Goniacz, R.: Safe exchange of information for civil-military operations. In: Military Communications and Information Technology: A Comprehensive Approach Enabler, pp. 39–50 (2011)Google Scholar
- 5.Apiecionek, Ł., Biernat, D., Makowski, W., Lukasik, M.: Practical implementation of AI for military airplane battlefield support system. In: 2015 8th International Conference on Human System Interactions (HSI), pp. 249–253. IEEE (2015)Google Scholar
- 6.Apiecionek, Ł., Czerniak, J.M., Zarzycki, H.: Protection tool for distributed denial of services attack. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. (eds.) BDAS 2014. CCIS, vol. 424, pp. 405–414. Springer, Heidelberg (2014)CrossRefGoogle Scholar
- 7.Apiecionek, L., Romantowski, M.: Security solution for cloud computing (2014)Google Scholar
- 8.Bradtke, S.J., Barto, A.G.: Learning to predict by the method of temporal differences. Mach. Learn. 22, 33–57 (1996). (Springer)MATHGoogle Scholar
- 9.Kosinski, W., Prokopowicz, P., Slezak, D.: On algebraic operations on fuzzy reals. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. Advances in Soft Computing, vol. 19, pp. 54–61. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 10.Kozielski, M., Skowron, A., Wróbel, Ł., Sikora, M.: Regression rulelearning for methane forecasting in coal mines. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 495–504. Springer, Heidelberg (2015)Google Scholar
- 11.Kruszynski, H., Kosowski, T., Apiecionek, L.: CID server JASMINE. In: V Communications Conference in Sieradz (2014)Google Scholar
- 12.Lojka, T., Zolota, M., Zolotová, I., et al.: Communication engine in human-machine alarm interface system. In: Sincak, P., Hartono, P., Vircikova, M., Vascak, J., Jaksa, R. (eds.) Emergent Trends in Robotics and Intelligent Systems. Advances in Intelligent Systems and Computing, pp. 129–136. Springer, Heidelberg (2015)Google Scholar
- 13.Vidhate, D., Kulkarni, P.: Cooperative machine learning with information fusion for dynamic decision making in diagnostic applications. In: 2012 International Conference on Advances in Mobile Network, Communication and its Applications (MNCAPPS), pp. 70–74. IEEE (2012)Google Scholar
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